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Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms

Author

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  • Roberts, G. O.
  • Smith, A. F. M.

Abstract

Markov chain Monte Carlo (MCMC) simulation methods are being used increasingly in statistical computation to explore and estimate features of likelihood surfaces and Bayesian posterior distributions. This paper presents simple conditions which ensure the convergence of two widely used versions of MCMC, the Gibbs sampler and Metropolis-Hastings algorithms.

Suggested Citation

  • Roberts, G. O. & Smith, A. F. M., 1994. "Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms," Stochastic Processes and their Applications, Elsevier, vol. 49(2), pages 207-216, February.
    • Handle: RePEc:eee:spapps:v:49:y:1994:i:2:p:207-216
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